Authors : D. Vaishnavi; P. Lakshmi Indira; R. Sree Vishnu Priya; V. Manasa; V. Roja

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/3ywp4hpn

Scribd : https://tinyurl.com/4aefkrwm

DOI : https://doi.org/10.38124/ijisrt/26apr1285

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Efficient water use significantly contributes to sustainable agricultural development. Common methods of irrigation using flooding techniques or timers do not change based on environmental conditions and therefore either overirrigate or under-irrigate the crop and do not allow for proper growth. The objective of this research paper is to propose a methodology for farming by using Internet of Things (IOT), machine learning and reinforcement learning. Sensors connected to an ESP32 microcontroller gather information like moisture level, temperature, humidity and rainfall. The collected data will be sent to a Flask application hosted in the cloud for analyzing and processing. An IOT framework using a Q-Learning algorithm takes input which identifies appropriate action and time for irrigation. The crop recommendation system also suggests what type of crops you should grow as per the type of soil and climatic conditions. All the data is saved in the cloud using a MongoDB atlas database, and it is showcased on a web-based dashboard for easy usage. Using such a mechanism will not only save time and water but will also ensure smart farming.

Keywords : Reinforcement Learning, Adaptive Irrigation Management, ESP32 Microcontroller-Based Edge Computing IoT, CloudBased Flask Application with the Mongodb Atlas Database.

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